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- W4288365510 abstract "The body pose of a person wearing a camera is of great interest for applications in augmented reality, healthcare, and robotics, yet much of the person's body is out of view for a typical wearable camera. We propose a learning-based approach to estimate the camera wearer's 3D body pose from egocentric video sequences. Our key insight is to leverage interactions with another person---whose body pose we can directly observe---as a signal inherently linked to the body pose of the first-person subject. We show that since interactions between individuals often induce a well-ordered series of back-and-forth responses, it is possible to learn a temporal model of the interlinked poses even though one party is largely out of view. We demonstrate our idea on a variety of domains with dyadic interaction and show the substantial impact on egocentric body pose estimation, which improves the state of the art. Video results are available at http://vision.cs.utexas.edu/projects/you2me/" @default.
- W4288365510 created "2022-07-29" @default.
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- W4288365510 date "2019-04-22" @default.
- W4288365510 modified "2023-10-16" @default.
- W4288365510 title "You2Me: Inferring Body Pose in Egocentric Video via First and Second Person Interactions" @default.
- W4288365510 doi "https://doi.org/10.48550/arxiv.1904.09882" @default.
- W4288365510 hasPublicationYear "2019" @default.
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